R Notebook for WMATA Metrobus Data

This is an R Markdown Notebook for analysis using data on the DC Bus System (WMATA Metrobus). The data were obtained here:

https://planitmetro.com/2016/11/16/data-download-metrobus-vehicle-location-data/

Control + Alt + Shift + m = rename in scope

Load the packages to be used.

Get the data.

First let’s check the working directory.

getwd()

[1] "/Users/mdturse/Desktop/Analytics/DCMetroBus"

Then, actually get the data.

The working directory was changed to /Users/mdturse/Desktop/Analytics/DCMetroBus/Bus AVL Oct 2016 inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.

Put the daily data together.

AllDays <- bind_rows(list(Oct03Raw, Oct04Raw, Oct05Raw, Oct06Raw, Oct07Raw),
                     .id = c("group")
                    )

Unequal factor levels: coercing to characterUnequal factor levels: coercing to characterUnequal factor levels: coercing to characterUnequal factor levels: coercing to characterUnequal factor levels: coercing to characterUnequal factor levels: coercing to character

# dim(AllDays)
str(AllDays)

'data.frame':   3119443 obs. of  18 variables:
 $ group            : chr  "1" "1" "1" "1" ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : chr  "LOOP" "LOOP" "LOOP" "LOOP" ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : chr  "10-3-16 6:06:47 AM" "10-3-16 6:07:50 AM" "10-3-16 6:09:47 AM" "10-3-16 6:10:24 AM" ...
 $ Departure_Time   : chr  "10-3-16 6:06:47 AM" "10-3-16 6:08:01 AM" "10-3-16 6:09:47 AM" "10-3-16 6:10:24 AM" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...

Deleting old data frames.

for (i in 3:7){
  rm(list = ls(pattern = paste0("Oct0", i, "Raw")
              )
    )
  
  message("Deleting Oct0", i, "Raw")
  }

Deleting Oct03Raw
Deleting Oct04Raw
Deleting Oct05Raw
Deleting Oct06Raw
Deleting Oct07Raw

Updating variable types.

Then, sorting the data and adding a RowNumber (to be used for identifying rows later in the analyses.)

AllDays$group <- factor(AllDays$group)

Inspecting the values of Stop_ID, and finding that it can take the values “” (blank) and “NULL”.

View(group_by(AllDays_Sorted,
              Stop_ID
             ) %>% 
       summarise(
         Cnt = n()
         ) %>% 
       arrange(Stop_ID)
    )
View(filter(AllDays_Sorted,
            is.na(Stop_ID) |
              Stop_ID == "" |
              Stop_ID == "NULL"
           ) %>% 
       arrange(Stop_Desc)
    )

Creating a table of distinct Stop_Desc values when Stop_ID is “” (blank) or “NULL”.

StopID_New <- filter(AllDays_Sorted,
                     is.na(Stop_ID) |
                       Stop_ID == "" |
                       Stop_ID == "NULL"
                    ) %>% 
  select(Stop_ID, Stop_Desc) %>% 
  distinct() %>% 
  arrange(Stop_ID, Stop_Desc) %>% 
  mutate(StopID_New = 1:nrow(.)
        )
View(StopID_New)

Creating a full updated table by filling in StopID_New for when Stop_ID is “” (blank) or NULL.

AllDays_StopIDNew <- left_join(AllDays_Sorted,
                               select(StopID_New,
                                      Stop_Desc,
                                      StopID_New
                                     ),
                               by = c("Stop_Desc" = "Stop_Desc")
                              ) %>% 
  mutate(StopID_Clean = ifelse(is.na(StopID_New),
                               Stop_ID,
                               StopID_New
                              ),
         StopID_Indicator = factor(ifelse(is.na(StopID_New),
                                          "ID_OK",
                                          "ID_Bad"
                                         )
                                  )
        )
rm(StopID_New)
rm(AllDays_Sorted)
str(AllDays_StopIDNew)

'data.frame':   3119443 obs. of  22 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 1 1 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 7 6 3 2 8 1 1 2 3 ...
 $ Stop_ID          : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ Event_Type       : int  4 5 4 4 4 3 3 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 2 3 3 3 1 1 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:07:50" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:08:01" ...
 $ Dwell_Time       : int  0 11 0 0 0 0 104 0 0 0 ...
 $ Delta_Time       : int  -177 -27 24 165 25 73 719 0 74 76 ...
 $ Odometer_Distance: int  43543 43543 45139 46418 50115 51074 51303 53836 55633 56163 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 253 97 276 15 119 100 89 274 104 ...
 $ RowNum_OG        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004572" "5004573" "5002210" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...

# View(tail(AllDays_StopIDNew, 500))
# View(filter(AllDays_StopIDNew,
#             Stop_Desc == "METROWAY ANNNOUCEMNT CORR"
#            )
#     )

Feature engineering.

Inspecting incidences of consecutive Stop_IDs. This is done because investigation showed that many conseutive events occurr at the same Stop_ID, but with various Dwell_Times, Odometer_Distances, etc. All of which affect calculations and analyses.

Create data on the runs (consecutive Stop_IDs).

StopID_Runs <- rle(AllDays_StopIDNew$StopID_Clean)
StopID_Runs$ends <- cumsum(StopID_Runs$lengths)
StopID_Runs$starts <- ifelse(is.na(lag(StopID_Runs$ends)
                                  ),
                             1,
                             lag(StopID_Runs$ends) + 1
                            )
str(StopID_Runs)

List of 4
 $ lengths: int [1:2809529] 2 1 1 1 1 2 1 1 1 1 ...
 $ values : chr [1:2809529] "5004572" "5004573" "5002210" "5002209" ...
 $ ends   : int [1:2809529] 2 3 4 5 6 8 9 10 11 12 ...
 $ starts : num [1:2809529] 1 3 4 5 6 7 9 10 11 12 ...
 - attr(*, "class")= chr "rle"

# class(StopID_Runs)
# 
# StopID_Runs_df <- data.frame(unclass(StopID_Runs))
# str(StopID_Runs_df)
# class(StopID_Runs_df)
# rm(StopID_Runs_df)

Trying to link data on RunsGroups with the original data (AllDays_Sorted). The goal is to select only one record per RunsGroup - that being the record with the longest Dwell_Time.

I attempted this computation using both data.frames (dplyr) and data.tables (data.table). However, with 2,809,062 rows in one dataset and 3,119,443 rows in the other dataset, the current computation time is over 5 days…so I’m trying a different strategy to only select the first record in a run.

# Create a RunsGroup variable for each run
# StopID_Runs_df$RunsGroup <- paste0("g", seq(1:nrow(StopID_Runs_df)
#                                            )
#                                   )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# StopID_Runs_df <- StopID_Runs_df %>% 
#   mutate(RowNum = row_number()
#         )
# 
# str(StopID_Runs_df)
# head(StopID_Runs_df, 25)
# tail(StopID_Runs_df, 25)
# 
# 
# # Converting to data.tables for, hopefully, improved performance (speed) in computation
# StopID_Runs_dt <- data.table(StopID_Runs_df)
# setkey(StopID_Runs_dt, RowNum)
# str(StopID_Runs_dt)
# 
# AllDays_Sorted_dt <- data.table(AllDays_Sorted)
# setkey(AllDays_Sorted_dt, RowNum_OG)
# str(AllDays_Sorted_dt)
# # rm(AllDays_Sorted_dt)
# 
# 
# # Actual loop to perform the computations and link to original data (AllDays_Sorted_dt)
# GroupData <- list()
# for(i in 1:nrow(StopID_Runs_dt)
#    ) {
#   assign(paste0("group_", i),
#            StopID_Runs_dt[RowNum == i, RunsGroup]
#           )
# 
#     #####  The code below is the same code as above, but done with dplyr  #####
# 
#     # assign(paste0("group_", i),
#   #        filter(StopID_Runs_df,
#   #               RowNum == i
#   #              ) %>% 
#   #          select(RunsGroup)
#   #       )
# 
#   assign(paste0("group_", i, "_start"),
#          StopID_Runs_dt[RowNum == i, starts]
#         )
# 
#   assign(paste0("group_", i, "_end"),
#          StopID_Runs_dt[RowNum == i, ends]
#         )
# 
#   assign(paste0("group_", i, "_rows"),
#          AllDays_Sorted_dt[RowNum_OG >= as.numeric(get(paste0("group_", i, "_start")
#                                                       )
#                                                   ) &
#                            RowNum_OG <= as.numeric(get(paste0("group_", i, "_end")
#                                                       )
#                                                   ),
#                            RunsGroup := as.character(get(paste0("group_", i)
#                                                         )
#                                                     )
#                           ]
# 
#     #####  The code below is the same as the code above, but done with dplyr  #####
# 
#          # filter(AllDays_Sorted,
#          #        between(RowNum_OG,
#          #                as.numeric(get(paste0("group_", i, "_start")
#          #                              )
#          #                          ),
#          #                as.numeric(get(paste0("group_", i, "_end")
#          #                              )
#          #                          )
#          #               )
#          #       ) %>% 
#          #   mutate(RunsGroup = as.character(get(paste0("group_", i)
#          #                                     )
#          #                                 )
#          #        )
#         )
# 
#   GroupData[[i]] <- get(paste0("group_", i, "_rows"))
# 
#   message("Processing Group ", i, " of 2,809,062")
# }
# 
# 
# GroupData_df <- rbind.fill(GroupData)
# str(GroupData_df)
# head(GroupData_df)
# tail(GroupData_df)
# # rm(GroupData_df)
# 
# 
# group_1
# group_1_start
# group_1_end
# group_1_rows
# group_2_rows
# group_3_rows
# group_50_rows
# str(group_50_rows)
# group_2809062_rows
# GroupData[[1]]
# GroupData[[50]]
# 
# 
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# #####  Testing Area (Below)  #####
# 
# # head(StopID_Runs$starts, 20)
# # head(AllDays_NewOrder$Stop_ID, 20)
# # 
# # 
# # dat <- as.data.frame(c(1,1,7,7,7,9,6,8,2,2,2,1,1,1,1,1))
# # colnames(dat)[1] <- "dat"
# # r <- rle(dat$dat)
# # dat$run <- rep(r$lengths, r$lengths)
# # dat$runLag <- lag(dat$run)
# # dat$cond <- rep(r$values, r$lengths)
# # dat
# # View(dat)

When consecutive Stop_ID occurrs, only take the first occurrence. This is done because the computation time to select only the record with the longest Dwell_Time for each run was too long (over 5 days).

This is probably less than ideal with regards to Dwell_Time, but should not make much difference for calculations of travel time, speed, etc.

AllDays_FirstStopID <- AllDays_StopIDNew[StopID_Runs$starts, ]
dim(AllDays_StopIDNew)

[1] 3119443      22

dim(AllDays_FirstStopID)

[1] 2809529      22

nrow(AllDays_StopIDNew) - nrow(AllDays_FirstStopID)

[1] 309914

rm(AllDays_StopIDNew)
rm(StopID_Runs)
str(AllDays_FirstStopID)

'data.frame':   2809529 obs. of  22 variables:
 $ group            : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID           : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route            : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt         : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction  : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence    : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc        : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type       : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description: Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time       : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time   : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time       : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time       : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance: int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude         : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude        : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading          : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG        : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New       : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean     : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...

Feature engineering.

Creating new variables.

AllDays_AddVars <- mutate(AllDays_FirstStopID,
                          Odometer_Distance_Mi = Odometer_Distance / 5280, #5,280 feet in 1 mile
                          Dwell_Time2 = as.numeric(Departure_Time - Event_Time),
                          Event_Time_Yr = as.integer(year(Event_Time)),
                          Event_Time_Mth = as.integer(month(Event_Time)),
                          Event_Time_Date = day(Event_Time),
                          Event_Time_Day = wday(Event_Time, label = TRUE),
                          Event_Time_Hr = hour(Event_Time),
                          Event_Time_Min = minute(Event_Time),
                          Event_Time_HrGroup = factor(ifelse(Event_Time_Hr < 3,
                                                             "Group0_2",
                                                      ifelse(Event_Time_Hr < 6,
                                                             "Group3_5",
                                                      ifelse(Event_Time_Hr < 9,
                                                             "Group6_8",
                                                      ifelse(Event_Time_Hr < 12,
                                                             "Group9_11",
                                                      ifelse(Event_Time_Hr < 15,
                                                             "Group12_14",
                                                      ifelse(Event_Time_Hr < 18,
                                                             "Group15_17",
                                                      ifelse(Event_Time_Hr < 21,
                                                             "Group18_20",
                                                      ifelse(Event_Time_Hr < 24,
                                                             "Group21_23"
                                                            )))))))),
                                                         levels = c("Group0_2",
                                                                    "Group3_5",
                                                                    "Group6_8",
                                                                    "Group9_11",
                                                                    "Group12_14",
                                                                    "Group15_17",
                                                                    "Group18_20",
                                                                    "Group21_23"
                                                                   ),
                                                         ordered = TRUE
                                                     )
                         )
rm(AllDays_FirstStopID)
str(AllDays_AddVars)

'data.frame':   2809529 obs. of  31 variables:
 $ group               : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID              : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route               : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt            : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction     : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence       : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID             : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc           : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type          : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description   : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time          : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time      : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time          : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time          : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance   : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude            : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude           : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading             : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG           : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New          : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean        : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator    : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Odometer_Distance_Mi: num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2         : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr       : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth      : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date     : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day      : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr       : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min      : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup  : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...

# group_by(AllDays_AddVars,
#          Event_Time_HrGroup
#         ) %>% 
#   summarise(Cnts = n()
#            )
# View(head(filter(AllDays_AddVars,
#                  Event_Time_Hr == 0
#                 ),
#           50
#          )
#     )
# View(head(AllDays_AddVars, 50))

Feature engineering.

Creating more variables. Creating a BusEvent row number for future identification purposes. Then, creating various variables to analyze distance traveled and speed.

AllDays_BusDay <- group_by(AllDays_AddVars,
                           Bus_ID,
                           Event_Time_Date
                          ) %>% 
  mutate(BusDay_EventNum = row_number(),  # used to identify Bus movements on a particular date
         
         Odometer_Distance_Lag1 = lag(Odometer_Distance),
         
         Latitude_L1 = lag(Latitude),
         Longitude_L1 = lag(Longitude),
         # Lat_Radian = Latitude*pi/180,
         # Long_Radian = Longitude*pi/180,
         # Lat_Radian_L1 = lag(Lat_Radian),
         # Long_Radian_L1 = lag(Long_Radian),
         
         # accounting for potential negative distances
         TravelDistance_Ft = ifelse(Odometer_Distance > Odometer_Distance_Lag1,
                                    Odometer_Distance - Odometer_Distance_Lag1,
                                    NA
                                   ),
         TravelDistance_Mi = TravelDistance_Ft / 5280, #5,280 feet in 1 mile
         
         # TravelDistance_Mi2 = gcd.hf(long1 = Long_Radian_L1,
         #                             lat1 = Lat_Radian_L1,
         #                             long2 = Long_Radian,
         #                             lat2 = Lat_Radian
         #                            ),
         
         TravelDistance_Mi_Hvrs = 
                              # ifelse((is.na(Longitude_L1) | is.na(Latitude_L1)
                              #        ),
                              #        NA,
                              distHaversine(cbind(Longitude_L1, Latitude_L1),
                                            cbind(Longitude, Latitude)
                                           ) * 0.000621371, # 0.000621371 miles = 1 meter
         
         # accounting for potential negative times
         TravelTime_Sec = as.numeric(ifelse(Event_Time > lag(Departure_Time),
                                            Event_Time - lag(Departure_Time),
                                            NA
                                           )
                                    ),
         TravelTime_Hr = TravelTime_Sec / 3600, # 3,600 seconds in 1 hour
         
         # accounting for potential negative or zero travel times
         SpeedAvg_Mph = ifelse(TravelTime_Hr > 0,
                               TravelDistance_Mi / TravelTime_Hr,
                               NA
                              ),
         
         Start_ID = lag(StopID_Clean),
         Start_Desc = lag(Stop_Desc),
         StartStop_ID = ifelse(is.na(Start_ID),
                               paste("NULL", StopID_Clean, sep = "--"),
                               paste(Start_ID, StopID_Clean, sep = "--")
                              )
        ) %>% 
  as.data.frame()

Inspecting for issues with StartStop_ID (where the value is either NA or contains NULL). They ONLY exist when BusDay_EventNum = 1 (which is by design). So everything looks OK.

View(group_by(AllDays_BusDay,
              StartStop_ID
             ) %>% 
       summarise(
         Cnt = n()
       ) %>% 
       arrange(desc(Cnt)
              )
    )
View(filter(AllDays_BusDay,
            (is.na(StartStop_ID) |
              str_detect(StartStop_ID, "NULL")
            ) &
              BusDay_EventNum != 1
           )
    )

Stats for StartStop_ID.

Quantiles_SS_dt <- group_by(AllDays_BusDay,
                            StartStop_ID
                           ) %>% 
  mutate(TD_Mi_SS_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SS_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SS_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SS_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SS_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SS_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()

Stats for StartStop_ID with Event_Time_HrGroup.

Quantiles_SSHG_dt <- group_by(Stats_StSt,
                              StartStop_ID,
                              Event_Time_HrGroup
                             ) %>% 
  mutate(TD_Mi_SSHG_q5 = quantile(x = TravelDistance_Mi, probs = 0.05, na.rm = TRUE),
         TD_Mi_SSHG_q95 = quantile(x = TravelDistance_Mi, probs = 0.95, na.rm = TRUE),
         TT_Sec_SSHG_q5 = quantile(x = TravelTime_Sec, probs = 0.05, na.rm = TRUE),
         TT_Sec_SSHG_q95 = quantile(x = TravelTime_Sec, probs = 0.95, na.rm = TRUE),
         TT_Hr_SSHG_q5 = quantile(x = TravelTime_Hr, probs = 0.05, na.rm = TRUE),
         TT_Hr_SSHG_q95 = quantile(x = TravelTime_Hr, probs = 0.95, na.rm = TRUE)
        ) %>% 
  data.table()

Feature engineering.

Creating a BusEventRoute row number, and a RouteAlt_Lag1 indicator for future identification purposes.

AllDays_BusDayRoute <- group_by(Stats_StSt_HrGrp,
                                Bus_ID,
                                Event_Time_Date,
                                Route
                               ) %>% 
  mutate(RouteAlt_Lag1 = lag(RouteAlt)  # used in future analyses to identify Route changes
         # Odometer_Distance_Lag1 = lag(Odometer_Distance),
         # 
         # # accounting for potential negative distances
         # TravelDistance_Ft = ifelse(Odometer_Distance >= Odometer_Distance_Lag1,
         #                            Odometer_Distance - Odometer_Distance_Lag1,
         #                            NA
         #                           ),
         # TravelDistance_Mi = TravelDistance_Ft / 5280, #5,280 feet in 1 mile
         # 
         # # accounting for potential negative times
         # TravelTime_Sec = as.numeric(ifelse(Event_Time >= lag(Departure_Time),
         #                                    Event_Time - lag(Departure_Time),
         #                                    NA
         #                                   )
         #                            ),
         # TravelTime_Hr = TravelTime_Sec / 3600, # 3,600 seconds in 1 hour
         # 
         # # accounting for potential negative or zero travel times
         # SpeedAvg_Mph = ifelse(TravelTime_Hr > 0,
         #                       TravelDistance_Mi / TravelTime_Hr,
         #                       NA
         #                      )
        ) %>% 
  data.frame()
str(AllDays_BusDayRoute)

'data.frame':   2809529 obs. of  93 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SS_Cnt          : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F        : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TT_Sec_SS_Mean        : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F      : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SS_Med         : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F       : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SS_Cnt         : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F       : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Hr_SS_Mean         : num  NaN 0.0599 0.0103 0.0162 0.0503 ...
 $ TT_Hr_SS_Mean_F       : num  NaN 0.0608 0.0103 0.0182 NaN ...
 $ TT_Hr_SS_Med          : num  NA 0.0621 0.0103 0.0182 0.0503 ...
 $ TT_Hr_SS_Med_F        : num  NA 0.0621 0.0103 0.0182 NA ...
 $ TT_Hr_SS_Cnt          : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Hr_SS_Cnt_F        : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TD_Mi_SSHG_q5         : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95        : num  NA 0.627 0.242 0.7 0.182 ...
 $ TT_Sec_SSHG_q5        : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95       : num  NA 276 37 25 190 ...
 $ TT_Hr_SSHG_q5         : num  NA 0.01656 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_q95        : num  NA 0.07653 0.01028 0.00694 0.05278 ...
 $ TD_Mi_SSHG_Mean       : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F     : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med        : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F      : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Cnt        : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F      : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TT_Sec_SSHG_Mean      : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F    : num  NaN 226 37 25 190 ...
 $ TT_Sec_SSHG_Med       : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F     : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Cnt       : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F     : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TT_Hr_SSHG_Mean       : num  NaN 0.05615 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Mean_F     : num  NaN 0.06278 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med        : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med_F      : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Cnt        : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Hr_SSHG_Cnt_F      : int  0 5 1 1 1 31 4 32 28 1 ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...

Feature engineering.

Calculating a variable to know if the RouteAlt changed. Could be useful in helping identifying weirdness in calculated distances and speeds.

rm(Stats_StSt_HrGrp)
AllDays_DirChange <- AllDays_BusDayRoute %>% 
  mutate(DirChange = ifelse(RouteAlt == RouteAlt_Lag1,
                            "Same",
                            "Change"
                           ),
         DirChange2 = factor(ifelse(is.na(DirChange),
                                    "Change",
                                    DirChange
                                   )
                            )
        )
str(AllDays_DirChange)

'data.frame':   2809529 obs. of  95 variables:
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Stop_ID               : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ StopID_New            : int  NA NA NA NA NA NA NA NA NA NA ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Latitude_L1           : num  NA 38.8 38.8 38.8 38.8 ...
 $ Longitude_L1          : num  NA -77.2 -77.2 -77.2 -77.2 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SS_Cnt          : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F        : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TT_Sec_SS_Mean        : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F      : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SS_Med         : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F       : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SS_Cnt         : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F       : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Hr_SS_Mean         : num  NaN 0.0599 0.0103 0.0162 0.0503 ...
 $ TT_Hr_SS_Mean_F       : num  NaN 0.0608 0.0103 0.0182 NaN ...
 $ TT_Hr_SS_Med          : num  NA 0.0621 0.0103 0.0182 0.0503 ...
 $ TT_Hr_SS_Med_F        : num  NA 0.0621 0.0103 0.0182 NA ...
 $ TT_Hr_SS_Cnt          : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Hr_SS_Cnt_F        : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TD_Mi_SSHG_q5         : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95        : num  NA 0.627 0.242 0.7 0.182 ...
 $ TT_Sec_SSHG_q5        : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95       : num  NA 276 37 25 190 ...
 $ TT_Hr_SSHG_q5         : num  NA 0.01656 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_q95        : num  NA 0.07653 0.01028 0.00694 0.05278 ...
 $ TD_Mi_SSHG_Mean       : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F     : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med        : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F      : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Cnt        : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F      : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TT_Sec_SSHG_Mean      : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F    : num  NaN 226 37 25 190 ...
 $ TT_Sec_SSHG_Med       : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F     : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Cnt       : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F     : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TT_Hr_SSHG_Mean       : num  NaN 0.05615 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Mean_F     : num  NaN 0.06278 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med        : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med_F      : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Cnt        : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Hr_SSHG_Cnt_F      : int  0 5 1 1 1 31 4 32 28 1 ...
 $ RouteAlt_Lag1         : Factor w/ 14 levels "1","10","11",..: NA 1 1 1 1 1 1 6 6 6 ...
 $ DirChange             : chr  NA "Same" "Same" "Same" ...
 $ DirChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...

Re-ordering the variables to ease with comprehension.

rm(AllDays_BusDayRoute)
AllDays_NewOrder <-  select(AllDays_DirChange,
                            RowNum_OG,
                            group,
                            StartStop_ID,
                            BusDay_EventNum,
                            Bus_ID,
                            Route,
                            RouteAlt,
                            # RouteAlt_Lag1,
                            DirChange2,
                            Route_Direction,
                            Stop_Sequence,
                            Start_ID,
                            Start_Desc,
                            # Stop_ID,
                            StopID_Clean,
                            StopID_Indicator,
                            Stop_Desc,
                            Event_Type,
                            Event_Description,
                            Event_Time_Yr,
                            Event_Time_Mth,
                            Event_Time_Date,
                            Event_Time_Day,
                            Event_Time_Hr,
                            Event_Time_HrGroup,
                            Event_Time_Min,
                            Event_Time,
                            Departure_Time,
                            Dwell_Time,
                            Dwell_Time2,
                            Delta_Time,
                            Latitude,
                            Longitude,
                            Heading,
                            Odometer_Distance,
                            Odometer_Distance_Lag1,
                            Odometer_Distance_Mi,
                            TravelDistance_Ft,
                            TravelDistance_Mi,
                            TravelDistance_Mi_Hvrs,
                            TD_Mi_SS_q5,
                            TD_Mi_SS_q95,
                            TD_Mi_SSHG_q5,
                            TD_Mi_SSHG_q95,
                            TD_Mi_SS_Mean,
                            TD_Mi_SS_Mean_F,
                            TD_Mi_SSHG_Mean,
                            TD_Mi_SSHG_Mean_F,
                            TD_Mi_SS_Med,
                            TD_Mi_SS_Med_F,
                            TD_Mi_SSHG_Med,
                            TD_Mi_SSHG_Med_F,
                            TD_Mi_SS_Cnt,
                            TD_Mi_SS_Cnt_F,
                            TD_Mi_SSHG_Cnt,
                            TD_Mi_SSHG_Cnt_F,
                            TravelTime_Sec,
                            TT_Sec_SS_q5,
                            TT_Sec_SS_q95,
                            TT_Sec_SSHG_q5,
                            TT_Sec_SSHG_q95,
                            TT_Sec_SS_Mean,
                            TT_Sec_SS_Mean_F,
                            TT_Sec_SSHG_Mean,
                            TT_Sec_SSHG_Mean_F,
                            TT_Sec_SS_Med,
                            TT_Sec_SS_Med_F,
                            TT_Sec_SSHG_Med,
                            TT_Sec_SSHG_Med_F,
                            TT_Sec_SS_Cnt,
                            TT_Sec_SS_Cnt_F,
                            TT_Sec_SSHG_Cnt,
                            TT_Sec_SSHG_Cnt_F,
                            TravelTime_Hr,
                            TT_Hr_SS_q5,
                            TT_Hr_SS_q95,
                            TT_Hr_SSHG_q5,
                            TT_Hr_SSHG_q95,
                            TT_Hr_SS_Mean,
                            TT_Hr_SS_Mean_F,
                            TT_Hr_SSHG_Mean,
                            TT_Hr_SSHG_Mean_F,
                            TT_Hr_SS_Med,
                            TT_Hr_SS_Med_F,
                            TT_Hr_SSHG_Med,
                            TT_Hr_SSHG_Med_F,
                            TT_Hr_SS_Cnt,
                            TT_Hr_SS_Cnt_F,
                            TT_Hr_SSHG_Cnt,
                            TT_Hr_SSHG_Cnt_F,
                            SpeedAvg_Mph
                           )
rm(AllDays_DirChange)
str(AllDays_NewOrder)

'data.frame':   2809529 obs. of  89 variables:
 $ RowNum_OG             : int  1 3 4 5 6 7 9 10 11 12 ...
 $ group                 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ StartStop_ID          : chr  "NULL--5004572" "5004572--5004573" "5004573--5002210" "5002210--5002209" ...
 $ BusDay_EventNum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Bus_ID                : int  11 11 11 11 11 11 11 11 11 11 ...
 $ Route                 : chr  "S80" "S80" "S80" "S80" ...
 $ RouteAlt              : Factor w/ 14 levels "1","10","11",..: 1 1 1 1 1 1 6 6 6 6 ...
 $ DirChange2            : Factor w/ 2 levels "Change","Same": 1 2 2 2 2 2 1 2 2 2 ...
 $ Route_Direction       : Factor w/ 12 levels "","ANTICLKW",..: 6 6 6 6 6 6 6 6 6 6 ...
 $ Stop_Sequence         : int  7 6 3 2 8 1 2 3 4 2 ...
 $ Start_ID              : chr  NA "5004572" "5004573" "5002210" ...
 $ Start_Desc            : chr  NA "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" ...
 $ StopID_Clean          : chr  "5004572" "5004573" "5002210" "5002209" ...
 $ StopID_Indicator      : Factor w/ 2 levels "ID_Bad","ID_OK": 2 2 2 2 2 2 2 2 2 2 ...
 $ Stop_Desc             : chr  "BEULAH ST + CHARLES ARRINGTON DR" "WALKER LN + #6363" "WALKER LN + BEULAH ST" "BEULAH ST + CHARLES ARRINGTON DR" ...
 $ Event_Type            : int  4 4 4 4 3 3 4 4 4 4 ...
 $ Event_Description     : Factor w/ 3 levels "Serviced Stop                                     ",..: 3 3 3 3 1 1 3 3 3 3 ...
 $ Event_Time_Yr         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
 $ Event_Time_Mth        : int  10 10 10 10 10 10 10 10 10 10 ...
 $ Event_Time_Date       : int  3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Day        : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Event_Time_Hr         : int  6 6 6 6 6 6 6 6 6 6 ...
 $ Event_Time_HrGroup    : Ord.factor w/ 8 levels "Group0_2"<"Group3_5"<..: 3 3 3 3 3 3 3 3 3 3 ...
 $ Event_Time_Min        : int  6 9 10 10 13 14 21 21 23 23 ...
 $ Event_Time            : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Departure_Time        : POSIXct, format: "2016-10-03 06:06:47" "2016-10-03 06:09:47" ...
 $ Dwell_Time            : int  0 0 0 0 0 104 0 0 0 0 ...
 $ Dwell_Time2           : num  0 0 0 0 0 104 0 0 0 0 ...
 $ Delta_Time            : int  -177 24 165 25 73 719 74 76 63 69 ...
 $ Latitude              : num  38.8 38.8 38.8 38.8 38.8 ...
 $ Longitude             : num  -77.2 -77.2 -77.2 -77.2 -77.2 ...
 $ Heading               : int  199 97 276 15 119 100 274 104 241 274 ...
 $ Odometer_Distance     : int  43543 45139 46418 50115 51074 51303 55633 56163 56285 57262 ...
 $ Odometer_Distance_Lag1: int  NA 43543 45139 46418 50115 51074 51303 55633 56163 56285 ...
 $ Odometer_Distance_Mi  : num  8.25 8.55 8.79 9.49 9.67 ...
 $ TravelDistance_Ft     : int  NA 1596 1279 3697 959 229 4330 530 122 977 ...
 $ TravelDistance_Mi     : num  NA 0.302 0.242 0.7 0.182 ...
 $ TravelDistance_Mi_Hvrs: num  NA 0.15 0.105 0.165 0.832 ...
 $ TD_Mi_SS_q5           : num  NA 0.0252 0.2422 0.7324 0.0794 ...
 $ TD_Mi_SS_q95          : num  NA 0.626 0.242 1.008 0.176 ...
 $ TD_Mi_SSHG_q5         : num  NA 0.0996 0.2422 0.7002 0.1816 ...
 $ TD_Mi_SSHG_q95        : num  NA 0.627 0.242 0.7 0.182 ...
 $ TD_Mi_SS_Mean         : num  NaN 0.437 0.242 0.908 0.128 ...
 $ TD_Mi_SS_Mean_F       : num  NaN 0.457 0.242 0.977 NaN ...
 $ TD_Mi_SSHG_Mean       : num  NaN 0.442 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Mean_F     : num  NaN 0.491 0.242 0.7 0.182 ...
 $ TD_Mi_SS_Med          : num  NA 0.512 0.242 0.962 0.128 ...
 $ TD_Mi_SS_Med_F        : num  NA 0.512 0.242 1.008 NA ...
 $ TD_Mi_SSHG_Med        : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SSHG_Med_F      : num  NA 0.512 0.242 0.7 0.182 ...
 $ TD_Mi_SS_Cnt          : int  0 14 1 4 2 87 22 118 91 11 ...
 $ TD_Mi_SS_Cnt_F        : int  0 12 1 3 0 77 18 106 81 9 ...
 $ TD_Mi_SSHG_Cnt        : int  0 7 1 1 1 23 6 29 28 3 ...
 $ TD_Mi_SSHG_Cnt_F      : int  0 5 1 1 1 19 4 25 24 1 ...
 $ TravelTime_Sec        : num  NA 180 37 25 190 29 288 52 76 8 ...
 $ TT_Sec_SS_q5          : num  NA 11.9 37 30.5 172.9 ...
 $ TT_Sec_SS_q95         : num  NA 346.3 37 75.8 189.1 ...
 $ TT_Sec_SSHG_q5        : num  NA 59.6 37 25 190 11.6 236 51.5 55 8.8 ...
 $ TT_Sec_SSHG_q95       : num  NA 276 37 25 190 ...
 $ TT_Sec_SS_Mean        : num  NaN 215.8 37 58.2 181 ...
 $ TT_Sec_SS_Mean_F      : num  NaN 218.9 37 65.5 NaN ...
 $ TT_Sec_SSHG_Mean      : num  NaN 202 37 25 190 ...
 $ TT_Sec_SSHG_Mean_F    : num  NaN 226 37 25 190 ...
 $ TT_Sec_SS_Med         : num  NA 223.5 37 65.5 181 ...
 $ TT_Sec_SS_Med_F       : num  NA 223.5 37 65.5 NA ...
 $ TT_Sec_SSHG_Med       : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SSHG_Med_F     : num  NA 219 37 25 190 134 286 60 65 16 ...
 $ TT_Sec_SS_Cnt         : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Sec_SS_Cnt_F       : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Sec_SSHG_Cnt       : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Sec_SSHG_Cnt_F     : int  0 5 1 1 1 31 4 32 32 1 ...
 $ TravelTime_Hr         : num  NA 0.05 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SS_q5           : num  NA 0.00331 0.01028 0.00849 0.04803 ...
 $ TT_Hr_SS_q95          : num  NA 0.0962 0.0103 0.0211 0.0525 ...
 $ TT_Hr_SSHG_q5         : num  NA 0.01656 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_q95        : num  NA 0.07653 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SS_Mean         : num  NaN 0.0599 0.0103 0.0162 0.0503 ...
 $ TT_Hr_SS_Mean_F       : num  NaN 0.0608 0.0103 0.0182 NaN ...
 $ TT_Hr_SSHG_Mean       : num  NaN 0.05615 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Mean_F     : num  NaN 0.06278 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SS_Med          : num  NA 0.0621 0.0103 0.0182 0.0503 ...
 $ TT_Hr_SS_Med_F        : num  NA 0.0621 0.0103 0.0182 NA ...
 $ TT_Hr_SSHG_Med        : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SSHG_Med_F      : num  NA 0.06083 0.01028 0.00694 0.05278 ...
 $ TT_Hr_SS_Cnt          : int  0 14 1 4 2 173 22 141 141 11 ...
 $ TT_Hr_SS_Cnt_F        : int  0 12 1 2 0 156 18 127 128 9 ...
 $ TT_Hr_SSHG_Cnt        : int  0 7 1 1 1 35 6 36 35 3 ...
 $ TT_Hr_SSHG_Cnt_F      : int  0 5 1 1 1 31 4 32 28 1 ...
 $ SpeedAvg_Mph          : num  NA 6.05 23.57 100.83 3.44 ...

# View(head(AllDays_NewOrder, 500))
View(tail(AllDays_NewOrder, 500))

Summarizing the data to help spot anomolies.

summary(AllDays_NewOrder)

   RowNum_OG       group      StartStop_ID       BusDay_EventNum      Bus_ID    
 Min.   :      1   1:559521   Length:2809529     Min.   :   1.0   Min.   :  11  
 1st Qu.: 784722   2:561389   Class :character   1st Qu.: 113.0   1st Qu.:2922  
 Median :1563300   3:567794   Mode  :character   Median : 248.0   Median :6195  
 Mean   :1562504   4:559180                      Mean   : 290.5   Mean   :5382  
 3rd Qu.:2337981   5:561645                      3rd Qu.: 428.0   3rd Qu.:7104  
 Max.   :3119443                                 Max.   :1344.0   Max.   :8105  
                                                                                
    Route              RouteAlt        DirChange2      Route_Direction  
 Length:2809529     2      :1128810   Change:  67071   SOUTH   :739235  
 Class :character   1      :1065425   Same  :2742458   NORTH   :735203  
 Mode  :character   3      : 260372                    WEST    :649706  
                    4      : 130801                    EAST    :628074  
                    5      :  75039                    LOOP    : 35611  
                    6      :  56408                    CLOCKWIS: 10671  
                    (Other):  92674                    (Other) : 11029  
 Stop_Sequence      Start_ID          Start_Desc        StopID_Clean      
 Min.   :  1.00   Length:2809529     Length:2809529     Length:2809529    
 1st Qu.: 12.00   Class :character   Class :character   Class :character  
 Median : 24.00   Mode  :character   Mode  :character   Mode  :character  
 Mean   : 26.83                                                           
 3rd Qu.: 39.00                                                           
 Max.   :104.00                                                           
                                                                          
 StopID_Indicator  Stop_Desc           Event_Type 
 ID_Bad:  18948   Length:2809529     Min.   :3.0  
 ID_OK :2790581   Class :character   1st Qu.:3.0  
                  Mode  :character   Median :4.0  
                                     Mean   :3.6  
                                     3rd Qu.:4.0  
                                     Max.   :5.0  
                                                  
                                          Event_Description   Event_Time_Yr 
 Serviced Stop                                     :1127366   Min.   :2016  
 Unknown Stop                                      :   2579   1st Qu.:2016  
 UnServiced Stop                                   :1679584   Median :2016  
                                                              Mean   :2016  
                                                              3rd Qu.:2016  
                                                              Max.   :2016  
                                                                            
 Event_Time_Mth Event_Time_Date Event_Time_Day Event_Time_Hr    Event_Time_HrGroup
 Min.   :10     Min.   :3.000   Sun  :     0   Min.   : 0.00   Group6_8  :611612  
 1st Qu.:10     1st Qu.:4.000   Mon  :559521   1st Qu.: 8.00   Group15_17:560103  
 Median :10     Median :5.000   Tues :561389   Median :13.00   Group18_20:461056  
 Mean   :10     Mean   :5.001   Wed  :567794   Mean   :12.97   Group9_11 :396514  
 3rd Qu.:10     3rd Qu.:6.000   Thurs:559180   3rd Qu.:18.00   Group12_14:353603  
 Max.   :10     Max.   :7.000   Fri  :561645   Max.   :23.00   Group21_23:244522  
                                Sat  :     0                   (Other)   :182119  
 Event_Time_Min    Event_Time                  Departure_Time               
 Min.   : 0.00   Min.   :2016-10-03 00:00:00   Min.   :2016-10-03 00:00:00  
 1st Qu.:14.00   1st Qu.:2016-10-04 08:36:14   1st Qu.:2016-10-04 08:36:20  
 Median :29.00   Median :2016-10-05 13:49:29   Median :2016-10-05 13:49:38  
 Mean   :29.43   Mean   :2016-10-05 13:29:21   Mean   :2016-10-05 13:29:28  
 3rd Qu.:44.00   3rd Qu.:2016-10-06 17:58:06   3rd Qu.:2016-10-06 17:58:13  
 Max.   :59.00   Max.   :2016-10-07 23:59:59   Max.   :2016-10-08 00:12:31  
                                                                            
   Dwell_Time       Dwell_Time2         Delta_Time         Latitude    
 Min.   :   0.00   Min.   :   0.000   Min.   :-5606.0   Min.   : 0.00  
 1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   14.0   1st Qu.:38.86  
 Median :   0.00   Median :   0.000   Median :  157.0   Median :38.90  
 Mean   :  12.56   Mean   :   6.359   Mean   :  268.8   Mean   :38.91  
 3rd Qu.:   5.00   3rd Qu.:   4.000   3rd Qu.:  396.0   3rd Qu.:38.96  
 Max.   :6205.00   Max.   :6205.000   Max.   : 9426.0   Max.   :39.19  
                                                                       
   Longitude         Heading      Odometer_Distance  Odometer_Distance_Lag1
 Min.   :-77.45   Min.   :  0.0   Min.   :       0   Min.   :       0      
 1st Qu.:-77.07   1st Qu.: 89.0   1st Qu.:  177595   1st Qu.:  177326      
 Median :-77.02   Median :180.0   Median :  377510   Median :  376934      
 Mean   :-77.02   Mean   :176.9   Mean   :  426254   Mean   :  425713      
 3rd Qu.:-76.97   3rd Qu.:269.0   3rd Qu.:  623667   3rd Qu.:  622879      
 Max.   :  0.00   Max.   :360.0   Max.   :11108034   Max.   :10853226      
                                                     NA's   :6528          
 Odometer_Distance_Mi TravelDistance_Ft TravelDistance_Mi TravelDistance_Mi_Hvrs
 Min.   :   0.00      Min.   :      1   Min.   :  0.0     Min.   : 0.000        
 1st Qu.:  33.64      1st Qu.:    699   1st Qu.:  0.1     1st Qu.: 0.106        
 Median :  71.50      Median :   1044   Median :  0.2     Median : 0.142        
 Mean   :  80.73      Mean   :   1624   Mean   :  0.3     Mean   : 0.201        
 3rd Qu.: 118.12      3rd Qu.:   1518   3rd Qu.:  0.3     3rd Qu.: 0.193        
 Max.   :2103.79      Max.   :1323464   Max.   :250.7     Max.   :24.407        
                      NA's   :322734    NA's   :322734    NA's   :6528          
  TD_Mi_SS_q5       TD_Mi_SS_q95     TD_Mi_SSHG_q5    TD_Mi_SSHG_q95  
 Min.   :  0.000   Min.   :  0.000   Min.   :  0.00   Min.   :  0.00  
 1st Qu.:  0.086   1st Qu.:  0.262   1st Qu.:  0.09   1st Qu.:  0.25  
 Median :  0.104   Median :  0.326   Median :  0.11   Median :  0.31  
 Mean   :  0.164   Mean   :  0.488   Mean   :  0.18   Mean   :  0.47  
 3rd Qu.:  0.139   3rd Qu.:  0.436   3rd Qu.:  0.15   3rd Qu.:  0.42  
 Max.   :219.163   Max.   :246.949   Max.   :250.66   Max.   :250.66  
 NA's   :24757     NA's   :24757     NA's   :35629    NA's   :35629   
 TD_Mi_SS_Mean     TD_Mi_SS_Mean_F   TD_Mi_SSHG_Mean  TD_Mi_SSHG_Mean_F
 Min.   :  0.000   Min.   :  0.000   Min.   :  0.00   Min.   :  0.00   
 1st Qu.:  0.172   1st Qu.:  0.166   1st Qu.:  0.17   1st Qu.:  0.16   
 Median :  0.212   Median :  0.207   Median :  0.21   Median :  0.21   
 Mean   :  0.307   Mean   :  0.291   Mean   :  0.31   Mean   :  0.29   
 3rd Qu.:  0.267   3rd Qu.:  0.260   3rd Qu.:  0.27   3rd Qu.:  0.26   
 Max.   :219.163   Max.   :219.163   Max.   :250.66   Max.   :250.66   
 NA's   :24757     NA's   :27919     NA's   :35629    NA's   :44458    
  TD_Mi_SS_Med     TD_Mi_SS_Med_F    TD_Mi_SSHG_Med   TD_Mi_SSHG_Med_F
 Min.   :  0.000   Min.   :  0.000   Min.   :  0.00   Min.   :  0.00  
 1st Qu.:  0.146   1st Qu.:  0.146   1st Qu.:  0.14   1st Qu.:  0.14  
 Median :  0.196   Median :  0.196   Median :  0.20   Median :  0.20  
 Mean   :  0.288   Mean   :  0.282   Mean   :  0.29   Mean   :  0.28  
 3rd Qu.:  0.265   3rd Qu.:  0.265   3rd Qu.:  0.27   3rd Qu.:  0.27  
 Max.   :219.163   Max.   :219.163   Max.   :250.66   Max.   :250.66  
 NA's   :24757     NA's   :27919     NA's   :35629    NA's   :44458   
  TD_Mi_SS_Cnt    TD_Mi_SS_Cnt_F   TD_Mi_SSHG_Cnt   TD_Mi_SSHG_Cnt_F
 Min.   :   0.0   Min.   :   0.0   Min.   :  0.00   Min.   :  0.00  
 1st Qu.: 163.0   1st Qu.: 146.0   1st Qu.: 26.00   1st Qu.: 22.00  
 Median : 280.0   Median : 252.0   Median : 45.00   Median : 39.00  
 Mean   : 347.4   Mean   : 312.7   Mean   : 57.27   Mean   : 50.85  
 3rd Qu.: 456.0   3rd Qu.: 411.0   3rd Qu.: 75.00   3rd Qu.: 67.00  
 Max.   :1543.0   Max.   :1388.0   Max.   :663.00   Max.   :595.00  
                                                                    
 TravelTime_Sec     TT_Sec_SS_q5      TT_Sec_SS_q95      TT_Sec_SSHG_q5    
 Min.   :    1.0   Min.   :    1.00   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   25.0   1st Qu.:   15.00   1st Qu.:   48.00   1st Qu.:   16.00  
 Median :   39.0   Median :   22.00   Median :   80.05   Median :   23.40  
 Mean   :  104.9   Mean   :   61.26   Mean   :  183.28   Mean   :   67.33  
 3rd Qu.:   72.0   3rd Qu.:   34.00   3rd Qu.:  134.60   3rd Qu.:   36.70  
 Max.   :60750.0   Max.   :60750.00   Max.   :60750.00   Max.   :60750.00  
 NA's   :6641      NA's   :6531       NA's   :6531       NA's   :6535      
 TT_Sec_SSHG_q95    TT_Sec_SS_Mean     TT_Sec_SS_Mean_F   TT_Sec_SSHG_Mean  
 Min.   :    1.00   Min.   :    1.00   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   43.80   1st Qu.:   29.06   1st Qu.:   27.54   1st Qu.:   28.38  
 Median :   72.95   Median :   44.16   Median :   41.91   Median :   43.38  
 Mean   :  169.21   Mean   :  104.88   Mean   :   91.34   Mean   :  104.88  
 3rd Qu.:  123.65   3rd Qu.:   73.30   3rd Qu.:   69.25   3rd Qu.:   72.93  
 Max.   :60750.00   Max.   :60750.00   Max.   :60750.00   Max.   :60750.00  
 NA's   :6535       NA's   :6531       NA's   :10519      NA's   :6535      
 TT_Sec_SSHG_Mean_F TT_Sec_SS_Med      TT_Sec_SS_Med_F    TT_Sec_SSHG_Med   
 Min.   :    1.00   Min.   :    1.00   Min.   :    1.00   Min.   :    1.00  
 1st Qu.:   27.21   1st Qu.:   26.00   1st Qu.:   26.00   1st Qu.:   26.00  
 Median :   41.48   Median :   39.00   Median :   39.00   Median :   39.00  
 Mean   :   93.53   Mean   :   91.55   Mean   :   84.82   Mean   :   94.94  
 3rd Qu.:   70.12   3rd Qu.:   65.00   3rd Qu.:   65.00   3rd Qu.:   67.00  
 Max.   :60750.00   Max.   :60750.00   Max.   :60750.00   Max.   :60750.00  
 NA's   :12811      NA's   :6531       NA's   :10519      NA's   :6535      
 TT_Sec_SSHG_Med_F  TT_Sec_SS_Cnt    TT_Sec_SS_Cnt_F  TT_Sec_SSHG_Cnt 
 Min.   :    1.00   Min.   :   0.0   Min.   :   0.0   Min.   :  0.00  
 1st Qu.:   26.00   1st Qu.: 194.0   1st Qu.: 177.0   1st Qu.: 29.00  
 Median :   38.50   Median : 310.0   Median : 282.0   Median : 51.00  
 Mean   :   88.44   Mean   : 384.4   Mean   : 349.8   Mean   : 63.46  
 3rd Qu.:   66.50   3rd Qu.: 497.0   3rd Qu.: 452.0   3rd Qu.: 83.00  
 Max.   :60750.00   Max.   :1664.0   Max.   :1523.0   Max.   :691.00  
 NA's   :12811                                                        
 TT_Sec_SSHG_Cnt_F TravelTime_Hr     TT_Hr_SS_q5      TT_Hr_SS_q95   
 Min.   :  0.00    Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.: 26.00    1st Qu.: 0.007   1st Qu.: 0.004   1st Qu.: 0.013  
 Median : 46.00    Median : 0.011   Median : 0.006   Median : 0.022  
 Mean   : 57.09    Mean   : 0.029   Mean   : 0.017   Mean   : 0.051  
 3rd Qu.: 74.00    3rd Qu.: 0.020   3rd Qu.: 0.009   3rd Qu.: 0.037  
 Max.   :634.00    Max.   :16.875   Max.   :16.875   Max.   :16.875  
                   NA's   :6641     NA's   :6531     NA's   :6531    
 TT_Hr_SSHG_q5    TT_Hr_SSHG_q95   TT_Hr_SS_Mean    TT_Hr_SS_Mean_F 
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.: 0.004   1st Qu.: 0.012   1st Qu.: 0.008   1st Qu.: 0.008  
 Median : 0.006   Median : 0.020   Median : 0.012   Median : 0.012  
 Mean   : 0.019   Mean   : 0.047   Mean   : 0.029   Mean   : 0.025  
 3rd Qu.: 0.010   3rd Qu.: 0.034   3rd Qu.: 0.020   3rd Qu.: 0.019  
 Max.   :16.875   Max.   :16.875   Max.   :16.875   Max.   :16.875  
 NA's   :6535     NA's   :6535     NA's   :6531     NA's   :10532   
 TT_Hr_SSHG_Mean  TT_Hr_SSHG_Mean_F  TT_Hr_SS_Med    TT_Hr_SS_Med_F  
 Min.   : 0.000   Min.   : 0.000    Min.   : 0.000   Min.   : 0.000  
 1st Qu.: 0.008   1st Qu.: 0.008    1st Qu.: 0.007   1st Qu.: 0.007  
 Median : 0.012   Median : 0.012    Median : 0.011   Median : 0.011  
 Mean   : 0.029   Mean   : 0.026    Mean   : 0.025   Mean   : 0.024  
 3rd Qu.: 0.020   3rd Qu.: 0.019    3rd Qu.: 0.018   3rd Qu.: 0.018  
 Max.   :16.875   Max.   :16.875    Max.   :16.875   Max.   :16.875  
 NA's   :6535     NA's   :12895     NA's   :6531     NA's   :10532   
 TT_Hr_SSHG_Med   TT_Hr_SSHG_Med_F  TT_Hr_SS_Cnt    TT_Hr_SS_Cnt_F  
 Min.   : 0.000   Min.   : 0.000   Min.   :   0.0   Min.   :   0.0  
 1st Qu.: 0.007   1st Qu.: 0.007   1st Qu.: 194.0   1st Qu.: 176.0  
 Median : 0.011   Median : 0.011   Median : 310.0   Median : 282.0  
 Mean   : 0.026   Mean   : 0.025   Mean   : 384.4   Mean   : 349.6  
 3rd Qu.: 0.019   3rd Qu.: 0.018   3rd Qu.: 497.0   3rd Qu.: 452.0  
 Max.   :16.875   Max.   :16.875   Max.   :1664.0   Max.   :1523.0  
 NA's   :6535     NA's   :12895                                     
 TT_Hr_SSHG_Cnt   TT_Hr_SSHG_Cnt_F  SpeedAvg_Mph    
 Min.   :  0.00   Min.   :  0.00   Min.   :    0.0  
 1st Qu.: 29.00   1st Qu.: 26.00   1st Qu.:   10.1  
 Median : 51.00   Median : 46.00   Median :   16.7  
 Mean   : 63.46   Mean   : 57.05   Mean   :   26.5  
 3rd Qu.: 83.00   3rd Qu.: 74.00   3rd Qu.:   31.2  
 Max.   :691.00   Max.   :634.00   Max.   :22924.1  
                                   NA's   :322762